Author:
Jurburg Stephanie D.,Buscot François,Chatzinotas Antonis,Chaudhari Narendrakumar M.,Clark Adam T.,Garbowski Magda,Grenié Matthias,Hom Erik F. Y.,Karakoç Canan,Marr Susanne,Neumann Steffen,Tarkka Mika,van Dam Nicole M.,Weinhold Alexander,Heintz-Buschart Anna
Abstract
AbstractThe measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Microbiology
Cited by
6 articles.
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